Abstract

Genetic algorithms (GAs) are multi-dimensional and stochastic search methods, involving complex interactions among their parameters. For last two decades, researchers have been trying to understand the mechanics of GA parameter interactions by using various techniques|careful `functional ' decomposition of parameter interactions, empirical studies, and Markov chain analysis. Although the complexities in these interactions are getting clearer with such analyses, it still remains an open question in the mind of a new-comer to the eld or to a GA-practitioner as to what values of GA parameters (such as population size, choice of GA operators, operator probabilities, and others) to use in an arbitrary problem. In this paper, we investigate the performance of simple tripartite GAs on a number of simple to complex test problems from a practical standpoint. Since in a real-world situation, the overall time to run a GA is more or less dominated by the time consumed by objective function evaluations, we compare di erent GAs for a xed number of function evaluations. Based on probability calculations and simulation results, it is observed that for solving simple problems (unimodal or small modality problems) the mutation operator plays an important role, although GAs with the crossover operator alone can also solve these problems. However, the two operators (when applied alone) have two di erent working zones for the population size. For complex problems involving massive multi-modality and misleadingness (deception), the crossover operator is the key search operator. Based on these studies, it is recommended that when in doubt, the use of the crossover operator with an adequate population size is a reliable approach.

Citations